Building Blocks

Healthcare Data

Evidence

Professor Richard Platt Interview

Dr Tom Foley, Dr Fergus Fairmichael

Background

Professor Platt is Chair of the Harvard Medical School Department of Population Medicine at the Harvard Pilgrim Health Care Institute. He has extensive experience in developing systems and capabilities for using routinely collected electronic health information to support public health surveillance, medical product safety assessments, comparative effectiveness and outcomes research, and quality improvement programs. Professor Platt also has experience in performing health system based intervention studies and observational studies using electronic medical record and claims data.

He is Principal Investigator of the FDA Mini-Sentinel program, which performs post-marketing safety surveillance using the electronic health data from over 125 million people. He is also principal investigator of the Coordinating Center of PCORI's National Patient Centered Clinical Research Network, PCORnet, a consortium of 29 networks that will use electronic health data to conduct comparative effectiveness research. He co-leads the Coordinating Center of the NIH Health Care System Research Collaboratory and leads a CDC Prevention Epicenter. Professor Platt has been principal investigator of a CDC Center of Excellence in Public Health Informatics, and an AHRQ Center for Education and Research on Therapeutics.

He is a member of the Institute of Medicine Roundtable on Value and Science Driven Healthcare and the Association of American Medical Colleges Advisory Panel on Research.

Interview Synopsis

Comparative Effectiveness Research (CER)

Routinely collected data, such as claims data, can be suitable for CER but this depends on each individual use case and the types of data. There is some value in what is routinely collected at present and this will improve as systems evolve.

Currently in US it is possible to examine certain outcomes with high confidence, as they are captured uniformly across multiple systems. Acute myocardial infarction or hip fracture requiring surgical repair are examples. There is good evidence that these events are captured and the data is sensitive and highly specific. It is then possible to associate these outcomes with various types of exposures or treatments.

With observational CER, it is possible to control for some confounding factors and not for others. Often a hybrid approach is required, where sophisticated automated analysis of thousands or millions of electronic records can be paired with a manual review of several hundred, to confirm accuracy. This technique was used successfully in a study looking at the link between rotavirus vaccine and intussusception. This is a powerful technique that could also be extended to patient reported outcomes.

Meaningful use and the adoption of standards

Meaningful use standards are currently in flux. It is unrealistic to ask the healthcare system to record information that is not required for the delivery of care.

The capability to rapidly perform sophisticated querying against a large dataset already exists. However, it is important to determine that data is fit for a particular purpose. Typically, specialised knowledge of the underlying data is currently required in order to develop queries that answer the intended question.

Workforce implications

There is potentially a role for a new type of professional who works between the clinician and other users of the data, “new medical librarians”.

Looking at the future

It is now possible to perform research that previously was not possible.

Over the next 2-3 years we hope to broaden the populations we are able to study using claims data, and to make progress in using electronic health records.

It is conceivable that natural language processing could progress quickly and become a useful adjunct for CER.